Imaging of objects is useful in a variety of contexts. In the medical context, imaging of patients plays an important role in numerous scenarios. Medical imaging of metabolic and biochemical activity within a patient is known as functional imaging. Functional imaging techniques include, for example, nuclear imaging such as Positron Emission Tomography (PET), Single Photon Computed Tomography (SPECT), functional magnetic resonance imaging (fMRI), and functional computed tomography (fCT). An overview of SPECT, PET systems, their combination with computer tomography (CT) systems as well as iterative image reconstruction for emission tomography is given in chapter 7, chapter 11, and chapter 21 of M. Wernick and J. Aarsvold, “Emission tomography: the fundamentals of PET and SPECT,” Elsevier Academic Press, 2004, the contents of which are herein incorporated by reference.
As just one example of medical imaging, SPECT imaging, is performed by using a gamma camera to acquire multiple projections in one space (e.g., 2D space) and then using a computer to perform tomographic image reconstruction to generate an image in a higher-dimensional (e.g., 3D or 4D) space. For example, a gamma photon-emitting radioisotope may be introduced into a patient's body, and any of various techniques can be used to bind the radioisotope to a location of interest in the body. The patient lies on a bed that is positioned at a given bed position. One or more gamma cameras are attached to the gantry, and the gantry rotates and/or shifts, causing the gamma camera(s) to rotate and/or shift relative to the patient. Detectors of the gamma camera(s) acquire projection data at each orientation by detecting gamma photons emitted by the radioisotope, resulting in a projection data set for this bed position.
In this manner, a portion of the body (e.g., heart) of the patient can be imaged to yield a 3D or 4D (e.g., three spatial dimensions plus time dimension) image that can be displayed in various ways, e.g., by showing various projections as requested by an operator. If imaging is then desired for another portion of the body (e.g., abdomen), it may be necessary to move the bed supporting the patient to a new bed position, so that the other portion of the body is now capable of being imaged. Multi-bed imaging has been used for this purpose. Traditionally, for multi-bed imaging, projection data are acquired for patient lying on a bed situated at a first bed position, and tomographic reconstruction is performed using that projection data to generate a first image. Then, the bed is moved to a second bed position. For convenience, this may be referred to as a second bed, although it is understood that the same bed has simply been moved to a new position. The first and second bed positions may also be referred to as first and second imaging positions because projection data used for imaging are acquired at those positions.
New projection data are acquired for the second bed (i.e., at the second imaging position), and reconstruction is performed using the new projection data to generate a second image. The axial edge typically has an inconsistency as the collimator with its 3D point response function (axial and transaxial) can receive counts from outside the field of view (FOV). This inconsistency is reflected in the image as artifacts. One technique for mitigating such artifacts is to simply restrict the useful axial FOV, or to employ techniques that only minimize the axial edge artifacts. When creating a volume out of multiple bed positions, such as the first and second bed positions described above, the image volumes are overlapped. The overlap function can either be a sharp cutoff or some interpolation.
One drawback of traditional multi-bed imaging is that combining two 3D images as described above often results in inconsistencies, both visually and quantitatively, at the interface of the regions of the patient's body corresponding to respective beds.